Differential analysis between PU.1-cobound and PU.1-unbound

ChIP-seq towards RUNX1 in THP-1 cells with PU.1 knockdown via shRNA or scramble controls. Experiment done in two batches. For peak calling, combine BAMs per batch and call against per-batch IgG. Then use IDR to get consensus between two batches. Merge and use as template for count matrix.

orthologs <- data.table::fread(paste0(outdir, "/human_mouse_orthologs.txt.gz"), data.table = FALSE)

signatures <- openxlsx::read.xlsx(paste0(outdir, "/Dataset_EV2_raw.xlsx"))

signature_human <-
  sapply(unique(signatures$signature), function(x) {
    dplyr::left_join(
      y = orthologs %>% mutate(gene = paste(mouse_id, mouse_name, sep = "_")),
      x = data.frame(gene = signatures[signatures$signature == x, "gene", drop = TRUE]), by = "gene"
    ) %>%
      dplyr::filter(!is.na(human_id) & !is.na(human_name) & !human_name == "") %>%
      dplyr::mutate(x = paste(human_id, human_name, sep = "_")) %>%
      pull(x) %>%
      unique()
  }, simplify = FALSE)

# Load RUNX1 peaks from THP.1
runx1_counts <- data.table::fread(
  paste0(rootdir, "/source_data/GSE251672_chipseq_runx1_rawCounts.tsv.gz"),
  data.table = FALSE
)

cnames <- colnames(runx1_counts[, 7:ncol(runx1_counts)])
cnames_split <- str_split_fixed(cnames, "_", 3)

fct_group <- factor(cnames_split[, 1])
fct_batch <- factor(cnames_split[, 3])

rgs <- str_split_fixed(runx1_counts$Geneid, ":|-", 3)
rgs <- GRanges(seqnames = rgs[, 1], ranges = IRanges(
  start = as.numeric(rgs[, 2]),
  end = as.numeric(rgs[, 3])
))

dds_runx1 <- DESeqDataSetFromMatrix(
  countData = runx1_counts[, 7:ncol(runx1_counts)],
  colData = DataFrame(condition = fct_group, batch = fct_batch),
  design = ~ condition + batch,
  rowRanges = rgs
)

vsd_runx1 <- DESeq2::vst(dds_runx1, blind = FALSE)
assayNames(vsd_runx1) <- "vst"

# Check clustering by PCA
runx_pca_data <- plotPCA(vsd_runx1, intgroup = c("condition", "batch"), returnData = TRUE)
runx_pca_pvar <- attributes(runx_pca_data)$percentVar

Appendix_Figure_S5A <-
  runx_pca_data %>%
  ggplot(aes(x = PC1, y = PC2, color = condition, shape = batch)) +
  geom_point(size = 2) +
  scale_color_manual(values = list.ggplot$colorblind_cols[c(5, 6)]) +
  xlab(paste0("PC1 (var.explained=", round(100 * runx_pca_pvar[1], 2), "%)")) +
  ylab(paste0("PC2 (var.explained=", round(100 * runx_pca_pvar[2], 2), "%)"))

Appendix_Figure_S5A

# Do differential analysis
rownames(dds_runx1) <- paste0("region_", 1:nrow(dds_runx1))
dds_runx1 <- DESeq(dds_runx1, parallel = FALSE)

#> estimating size factors

#> estimating dispersions

#> gene-wise dispersion estimates

#> mean-dispersion relationship

#> final dispersion estimates

#> fitting model and testing

# Use these sifa factors to scale raw bigwig files prior to averaging per condition
sf_runx <-
  as.data.frame(sizeFactors(dds_runx1)) %>%
  rownames_to_column("x") %>%
  setNames(c("Sample", "SizeFactors"))

# These size factors can be used to normalize bigwig tracks, so basically divide column 4 of the bedGraph by this and then convert to BW for viz
# write.table(sf_runx, paste0(outdir, "/lists/chipseq_runx1_thp1_sizeFactors.txt"),
#            col.names=TRUE, row.names=FALSE, sep="\t", quote=FALSE)

# lfc log2(1.1) enriches for logFCs somewhat beyond 1.2 -- just to get rid of tiny significant logFCs
res_runx1 <- results(dds_runx1, contrast = c("condition", "knockdown", "control"), lfcThreshold = log2(1.1))
lfc_runx1 <- lfcShrink(dds = dds_runx1, res = res_runx1, contrast = c("condition", "knockdown", "control"), type = "ashr")

#> using 'ashr' for LFC shrinkage. If used in published research, please cite:
#>     Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
#>     https://doi.org/10.1093/biostatistics/kxw041

Appendix_Figure_5B <-
  ggMAplot(lfc_runx1$log2FoldChange, -log10(lfc_runx1$padj), lfc_runx1$padj, preset = "volcano", quantiles.y = c(0, 1)) + guides(color = guide_legend(ncol = 1))

Appendix_Figure_5B

# Add PU.1 binding info directly from ReMap2022 database
con <- gzcon(url("https://remap.univ-amu.fr/storage/remap2022/hg38/MACS2/DATASET/GSE128834.SPI1.THP-1.bed.gz"))
txt <- readLines(con)
thp1_pu1 <-
  read.delim(textConnection(txt), header = FALSE) %>%
  makeGRangesFromDataFrame(
    seqnames.field = "V1", start.field = "V2", end.field = "V3", starts.in.df.are.0based = TRUE,
    keep.extra.columns = FALSE
  )

dds_runx1_overlap_pu1 <- subsetByOverlaps(dds_runx1, thp1_pu1)

lfc_runx1$pu1 <- rownames(lfc_runx1) %in% rownames(subsetByOverlaps(dds_runx1, thp1_pu1))

Figure_7B <- 
  lfc_runx1 %>%
  as.data.frame() %>%
  dplyr::filter(padj < 0.05) %>%
  mutate(pu1n = as.numeric(pu1)) %>%
  ggplot(aes(x = -pu1n, y = log2FoldChange, group = pu1)) +
  ggpointdensity::geom_pointdensity(size = .1, position = position_jitter(width = .25, seed = 1)) +
  viridis::scale_color_viridis(name = "density") +
  scale_x_continuous(breaks = c(-1, 0), labels = c("PU.1 co-bound", "PU.1 un-bound")) +
  xlab("") +
  ylab("log2FoldChange") +
  guides(x = guide_axis(angle = 45))

Figure_7B

#---------------------------
# Check if regions with more RUNX1 after PU.1 knockdown have a preference for signature 3/4 vs 1/2
#---------------------------

# First, strict analysis by window-based intersection
regions_more_runx1 <- lfc_runx1[lfc_runx1$padj < 0.05 & lfc_runx1$log2FoldChange > 0, ]
regions_less_runx1 <- lfc_runx1[lfc_runx1$padj < 0.05 & lfc_runx1$log2FoldChange < 0, ]

gr_more_runx1 <- rowRanges(dds_runx1[rownames(regions_more_runx1), ])
mcols(gr_more_runx1) <- NULL
mcols(gr_more_runx1) <- DataFrame(regions_more_runx1)

# For next step get coordinates of TSS of human GENCODE v34
gencode_v34_gtf <- "https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_34/gencode.v34.annotation.gtf.gz"

human_coords <-
  rtracklayer::import(gencode_v34_gtf) %>%
  data.frame() %>%
  dplyr::filter(type == "transcript") %>%
  dplyr::select(seqnames, start, end, strand, gene_id, gene_name) %>%
  dplyr::mutate(gene_id = gsub("\\..*", "", gene_id), gene = paste(gene_id, gene_name, sep = "_")) %>%
  makeGRangesFromDataFrame(., keep.extra.columns = TRUE)

# Take signature TSS of human homologs and define a window
coords_signatures <- sapply(paste0("signature", 1:4), function(x) {
  hc <- human_coords[human_coords$gene %in% signature_human[[x]]]
  hc <- resize(hc, fix = "start", width = 20000)

  return(hc)
}, simplify = FALSE)

#------------------------------
# GSEA-like analysis. Use the RUNX1 peaks in the window around the signature genes, that are not PU.1-bound
# and treat as a gene set, then use fgsea to assess whether globally signatures 1 and 2 are different in terms of RUNX1
# binding upon PU.1 knockdown compared to signature 3+4.
#------------------------------

region_sets <- sapply(names(coords_signatures), function(x) {

  # Overlap between window and non-PU.1-bound regions
  current <- coords_signatures[[x]]
  olap <- dds_runx1[dds_runx1 %over% current]
  olap <- olap[!olap %over% thp1_pu1]
  region_set <- rownames(olap)

  return(region_set)
}, simplify = FALSE)

names(region_sets) <- paste0(names(region_sets), "_no.PU1")

r <-
  as.data.frame(lfc_runx1) %>%
  arrange(-log2FoldChange) %>%
  mutate(region = rownames(.)) %>%
  dplyr::select(region, log2FoldChange)

ranking <- r$log2FoldChange
names(ranking) <- r$region

set.seed(1)
fg_runx <- fgsea::fgseaMultilevel(pathways = region_sets, stats = ranking, minSize = 0, maxSize = 99999, eps = 0)

data_runx_fgsea_s1_to_4 <-
  rbind(
    data.frame(plot_fgsea(pathway = region_sets$signature1_no.PU1, stats = ranking)$data, group = "signature1_no.PU1"),
    data.frame(plot_fgsea(pathway = region_sets$signature2_no.PU1, stats = ranking)$data, group = "signature2_no.PU1"),
    data.frame(plot_fgsea(pathway = region_sets$signature3_no.PU1, stats = ranking)$data, group = "signature3_no.PU1"),
    data.frame(plot_fgsea(pathway = region_sets$signature4_no.PU1, stats = ranking)$data, group = "signature4_no.PU1")
  ) %>%
  dplyr::left_join(x = ., y = data.frame(group = fg_runx$pathway, fdr = fg_runx$padj), by = "group")

formatted_fdr <- sapply(data_runx_fgsea_s1_to_4$fdr, function(p) {
  rounded_p <- signif(p, digits = 3)

  if (rounded_p < 0.001) {
    sprintf("%.2e", rounded_p)
  } else {
    sprintf("%.2f", rounded_p)
  }
})

data_runx_fgsea_s1_to_4$fdr <- formatted_fdr
data_runx_fgsea_s1_to_4 <-
  data_runx_fgsea_s1_to_4 %>%
  dplyr::left_join(x = ., y = as.data.frame(fg_runx) %>% rename(group = pathway), by = "group") %>%
  # mutate(group=paste(group, paste0("(NES = ", sprintf("%.3f", round(NES, 3)), " -- FDR = ", fdr, ")"))) %>%
  mutate(
    PU.1 = factor(
      if_else(grepl("no.P", group), "PU.1-unbound", "PU.1-bound"),
      levels = c("PU.1-unbound", "PU.1-bound")
    ),
    group = gsub("_.*", "", group)
  )

# The fGSEA stats
knitr::kable(unique(data_runx_fgsea_s1_to_4[, c(3:10, 12)]))

# The fGSEa-style plots (somewhat different colors than in the paper because one reviewer did not like the yellow)
Figure_7E <- 
  data_runx_fgsea_s1_to_4 %>%
  filter(PU.1 == "PU.1-unbound") %>%
  ggplot(aes(x = x, y = y, color = group)) +
  geom_line() +
  theme(legend.position = "top", legend.justification = "left") +
  scale_color_manual(values = list.ggplot$colorblind_cols, name = "") +
  guides(color = guide_legend(ncol = 1)) +
  xlab("ranked RUNX1 peaks") +
  ylab("enrichment score") +
  facet_wrap(~PU.1)

Figure_7E

Integration with published ChIP-seq

Use the loci that are differential in RUNX1 upon PU.1 knockdown. Use this as a basis to show RUNX1, PU.1 H3K4me1, H3K4me3 and H3K27ac ChIP-seq and ATAC-seq data.

This code chunk will not be run, simply because the necessary bigwig files are too big to be shared.

regions_more_runx1 <- lfc_runx1[lfc_runx1$padj < 0.05 & lfc_runx1$log2FoldChange > 0, ]
regions_less_runx1 <- lfc_runx1[lfc_runx1$padj < 0.05 & lfc_runx1$log2FoldChange < 0, ]

r_more <- rowRanges(dds_runx1)[rownames(regions_more_runx1)]
r_more$status <- "more"
r_less <- rowRanges(dds_runx1)[rownames(regions_less_runx1)]
r_less$status <- "less"

r_runx <- c(r_more, r_less)

ExtendSize <- 2500
targets <- r_runx
targets.extended <- resize(r_runx, fix = "center", width = ExtendSize * 2)

# Will not run this part here, since it takes the full (and quite big) bigwig files which are not easy to share.
# The code takes these, and only reads into R those ranges that intersect with our loci where we found differential RUNX1 binding.
# We share the output in the GitHub repository as bigwig_signals.rds and load this here. Code below is for documentation

if(2 < 1){
    
  list_bigwigs <- c(
    paste0(rootdir, "/source_data/published_epigenomics/GSE128834/PU.1_extended_unscaled.bigwig"), # PU.1 ChIP-seq normal THP-1
    paste0(rootdir, "/source_data/runx1_chipseq_bigwig/knockdown_extended_averaged.bigwig"), # RUNX1 ChIP-seq in THP-1 with PU.1 knockdown
    paste0(rootdir, "/source_data/runx1_chipseq_bigwig/control_extended_averaged.bigwig"), # as above but non-targeting control
    paste0(rootdir, "/source_data/published_epigenomics/GSE117864/H3K27ac_extended_unscaled.bigwig"), # H3K27ac in normal THP-1
    paste0(rootdir, "/source_data/published_epigenomics/GSE96800/THP1_rep2_extended_unscaled.bigwig") # ATAC-seq in normal THP-1
  )
  
  sapply(list_bigwigs, file.exists, simplify = FALSE)
  
  # sapply(list_bigwigs, file.exists)
  bigwigs_all <- bplapply(list_bigwigs, BPPARAM = bpparam, function(x) {
    b <- rtracklayer::import(x,
      format = "BigWig",
      selection = BigWigSelection(targets.extended)
    )
    b$score_log2 <- log2(b$score + 1)
    b$score <- NULL
    b
  })
  
  names(bigwigs_all) <- c("PU.1", "RUNX1 (PU.1 knockdown)", "RUNX1 (NTC)", "H3K27ac", "ATAC-seq")
  
  saveRDS(bigwigs_all, paste0(outdir, "/bigwig_signals.rds"), compress = FALSE)
  # and then xz-compress with -9 -e to make as small as possible
  
} else {
  
  bigwigs_all <- readRDS(paste0(outdir, "/bigwig_signals.rds.xz"))
  
}

# Transform bigwig selections (GRanges) to a matrix as required for the plotting function
nms <- lapply(names(bigwigs_all), function(x) {
  normMatrix <- normalizeToMatrix(
    signal = bigwigs_all[[x]],
    target = resize(targets, fix = "center", width = 1),
    background = 0, keep = c(0, 0.975),
    target_ratio = 0, mean_mode = "w0",
    value_column = "score_log2", extend = ExtendSize
  )

  normMatrix
})

names(nms) <- names(bigwigs_all)

fs <- 15
hm_modify <- grep("gp$", names(ht_opt), value = TRUE)
for (i in hm_modify) {
  ht_opt[[i]] <- gpar(fontsize = fs)
}

# Ensure row order is the same for all heatmaps
r_order <- order(enriched_score(nms$`RUNX1 (NTC)`), decreasing = TRUE)

# Save each heatmap individually, create final figure then in inkscape.
# Too painful to make this "publication-ready" right from inside R.
hms <- lapply(names(nms), function(x) {
  normMatrix <- nms[[x]]

  hm_text2 <- 4

  splitter <- gsub("less", "decreased RUNX1 binding", gsub("more", "increased RUNX1 binding", targets$status))
  splitter <- factor(splitter, levels = c("increased RUNX1 binding", "decreased RUNX1 binding"))

  hm_cols_annot <- c("#E69F00", "#56B4E9")
  names(hm_cols_annot) <- levels(splitter)

  ra <- rowAnnotation(RUNX1 = anno_block(
    gp = gpar(fill = c("#E69F00", "#56B4E9")),
    labels = levels(splitter),
    labels_gp = gpar(col = "black")
  ))


  ta <- HeatmapAnnotation(enriched = anno_enriched(
    gp = gpar(lty = 1, lwd = 3, col = hm_cols_annot), axis_param = list(gp = gpar(fontsize = fs))
  ))

  EH <- EnrichedHeatmap(
    mat = normMatrix,
    row_order = r_order,
    pos_line = FALSE,
    column_title = x,
    use_raster = TRUE,
    left_annotation = ra,
    rect_gp = gpar(col = "transparent"),
    heatmap_legend_param = list(
      # title_position = "leftcenter-rot",
      legend_direction = "horizontal",
      title = "norm. counts [log2]"
    ),
    col = c("dodgerblue", "black", "yellow"), # viridis::viridis(100), #c("black", "yellow"),
    top_annotation = ta,
    show_row_names = FALSE,
    row_split = splitter,
    row_title = NULL,
    name = "rel.signal"
  )

  pdf(NULL)
  EH <- draw(EH, heatmap_legend_side = "bottom")
  dev.off()

  return(EH)
})

names(hms) <- gsub("\\)", "", gsub("\\(|\\.| ", "_", names(nms)))

for (i in names(hms)) {
  assign(x = paste0("plot__heatmap_runx1_chipseq_integrated_with_", make.names(i)), hms[[i]])
}

# Figure 7D is Inkscape-fu with these five subplots
Figure_7A_1 <- plot__heatmap_runx1_chipseq_integrated_with_PU_1
Figure_7A_1

Figure_7A_2 <- plot__heatmap_runx1_chipseq_integrated_with_RUNX1__NTC
Figure_7A_2

Figure_7A_3 <- plot__heatmap_runx1_chipseq_integrated_with_RUNX1__PU_1_knockdown
Figure_7A_3

Figure_7A_4 <- plot__heatmap_runx1_chipseq_integrated_with_H3K27ac
Figure_7A_4

Figure_7A_5 <- plot__heatmap_runx1_chipseq_integrated_with_ATAC.seq
Figure_7A_5

Motif enrichment

a <- getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38, r_more) # more Runx1 after PU.1 KD
b <- getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38, r_less) # less PU.1 after PU.1 KD

if (!file.exists(paste0(outdir, "/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme"))) {
  download.file(
    url = "https://hocomoco11.autosome.org/final_bundle/hocomoco11/core/HUMAN/mono/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme",
    destfile = paste0(outdir, "/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme")
  )
}

ame_out <- paste0(outdir, "/ame/")
suppressWarnings(dir.create(ame_out))

ame <- memes::runAme(
  input = a, control = b, database = paste0(outdir, "/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme"),
  evalue_report_threshold = .Machine$integer.max,
  meme_path = "/software/meme/bin/", silent = FALSE,
  outdir = ame_out
)

#> 

#> Added /projectdir//analysis_results/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme to motif_sources which now has 1 file names.
#> Motif file name is /projectdir//analysis_results/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme.
#> The output directory '/projectdir//analysis_results//ame/' already exists.
#> Its contents will be overwritten.
#> E-value threshold for reporting results: 2.14748e+09
#> Checking alphabets in 1 motif files.
#> Loading motifs from file '/projectdir//analysis_results/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme'
#> Loading primary sequences.
#> Loading control sequences.
#> Not in partition maximization mode. Fixing partition at the number of primary sequences (4091).
#> MOTIF: 1 SEQ: 1000/7973
MOTIF: 1 SEQ: 2000/7973
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#> Sorting sequences by sequence PWM score to get PWM ranks; breaking ties to put negatives first.
#> Leaving sequences sorted by PWM score.
#> Optimizing over sequence PWM score threshold.
#> MOTIF: 2 SEQ: 1000/7973
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amf <- memes::runAme(
  input = b, control = a, database = paste0(outdir, "/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme"),
  evalue_report_threshold = .Machine$integer.max,
  meme_path = "/software/meme/bin/", silent = FALSE,
  outdir = ame_out
)

#> 

#> Added /projectdir//analysis_results/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme to motif_sources which now has 1 file names.
#> Motif file name is /projectdir//analysis_results/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme.
#> The output directory '/projectdir//analysis_results//ame/' already exists.
#> Its contents will be overwritten.
#> E-value threshold for reporting results: 2.14748e+09
#> Checking alphabets in 1 motif files.
#> Loading motifs from file '/projectdir//analysis_results/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme'
#> Loading primary sequences.
#> Loading control sequences.
#> Not in partition maximization mode. Fixing partition at the number of primary sequences (3882).
#> MOTIF: 1 SEQ: 1000/7973
MOTIF: 1 SEQ: 2000/7973
MOTIF: 1 SEQ: 3000/7973
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MOTIF: 1 SEQ: 7973/7973
#> Sorting sequences by sequence PWM score to get PWM ranks; breaking ties to put negatives first.
#> Leaving sequences sorted by PWM score.
#> Optimizing over sequence PWM score threshold.
#> MOTIF: 2 SEQ: 1000/7973
MOTIF: 2 SEQ: 2000/7973
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MOTIF: 390 SEQ: 1000/7973
MOTIF: 390 SEQ: 2000/7973
MOTIF: 390 SEQ: 3000/7973
MOTIF: 390 SEQ: 4000/7973
MOTIF: 390 SEQ: 5000/7973
MOTIF: 390 SEQ: 6000/7973
MOTIF: 390 SEQ: 7000/7973
MOTIF: 390 SEQ: 7973/7973
MOTIF: 391 SEQ: 1000/7973
MOTIF: 391 SEQ: 2000/7973
MOTIF: 391 SEQ: 3000/7973
MOTIF: 391 SEQ: 4000/7973
MOTIF: 391 SEQ: 5000/7973
MOTIF: 391 SEQ: 6000/7973
MOTIF: 391 SEQ: 7000/7973
MOTIF: 391 SEQ: 7973/7973
MOTIF: 392 SEQ: 1000/7973
MOTIF: 392 SEQ: 2000/7973
MOTIF: 392 SEQ: 3000/7973
MOTIF: 392 SEQ: 4000/7973
MOTIF: 392 SEQ: 5000/7973
MOTIF: 392 SEQ: 6000/7973
MOTIF: 392 SEQ: 7000/7973
MOTIF: 392 SEQ: 7973/7973
MOTIF: 393 SEQ: 1000/7973
MOTIF: 393 SEQ: 2000/7973
MOTIF: 393 SEQ: 3000/7973
MOTIF: 393 SEQ: 4000/7973
MOTIF: 393 SEQ: 5000/7973
MOTIF: 393 SEQ: 6000/7973
MOTIF: 393 SEQ: 7000/7973
MOTIF: 393 SEQ: 7973/7973
MOTIF: 394 SEQ: 1000/7973
MOTIF: 394 SEQ: 2000/7973
MOTIF: 394 SEQ: 3000/7973
MOTIF: 394 SEQ: 4000/7973
MOTIF: 394 SEQ: 5000/7973
MOTIF: 394 SEQ: 6000/7973
MOTIF: 394 SEQ: 7000/7973
MOTIF: 394 SEQ: 7973/7973
MOTIF: 395 SEQ: 1000/7973
MOTIF: 395 SEQ: 2000/7973
MOTIF: 395 SEQ: 3000/7973
MOTIF: 395 SEQ: 4000/7973
MOTIF: 395 SEQ: 5000/7973
MOTIF: 395 SEQ: 6000/7973
MOTIF: 395 SEQ: 7000/7973
MOTIF: 395 SEQ: 7973/7973
MOTIF: 396 SEQ: 1000/7973
MOTIF: 396 SEQ: 2000/7973
MOTIF: 396 SEQ: 3000/7973
MOTIF: 396 SEQ: 4000/7973
MOTIF: 396 SEQ: 5000/7973
MOTIF: 396 SEQ: 6000/7973
MOTIF: 396 SEQ: 7000/7973
MOTIF: 396 SEQ: 7973/7973
MOTIF: 397 SEQ: 1000/7973
MOTIF: 397 SEQ: 2000/7973
MOTIF: 397 SEQ: 3000/7973
MOTIF: 397 SEQ: 4000/7973
MOTIF: 397 SEQ: 5000/7973
MOTIF: 397 SEQ: 6000/7973
MOTIF: 397 SEQ: 7000/7973
MOTIF: 397 SEQ: 7973/7973
MOTIF: 398 SEQ: 1000/7973
MOTIF: 398 SEQ: 2000/7973
MOTIF: 398 SEQ: 3000/7973
MOTIF: 398 SEQ: 4000/7973
MOTIF: 398 SEQ: 5000/7973
MOTIF: 398 SEQ: 6000/7973
MOTIF: 398 SEQ: 7000/7973
MOTIF: 398 SEQ: 7973/7973
MOTIF: 399 SEQ: 1000/7973
MOTIF: 399 SEQ: 2000/7973
MOTIF: 399 SEQ: 3000/7973
MOTIF: 399 SEQ: 4000/7973
MOTIF: 399 SEQ: 5000/7973
MOTIF: 399 SEQ: 6000/7973
MOTIF: 399 SEQ: 7000/7973
MOTIF: 399 SEQ: 7973/7973
MOTIF: 400 SEQ: 1000/7973
MOTIF: 400 SEQ: 2000/7973
MOTIF: 400 SEQ: 3000/7973
MOTIF: 400 SEQ: 4000/7973
MOTIF: 400 SEQ: 5000/7973
MOTIF: 400 SEQ: 6000/7973
MOTIF: 400 SEQ: 7000/7973
MOTIF: 400 SEQ: 7973/7973
MOTIF: 401 SEQ: 1000/7973
MOTIF: 401 SEQ: 2000/7973
MOTIF: 401 SEQ: 3000/7973
MOTIF: 401 SEQ: 4000/7973
MOTIF: 401 SEQ: 5000/7973
MOTIF: 401 SEQ: 6000/7973
MOTIF: 401 SEQ: 7000/7973
MOTIF: 401 SEQ: 7973/7973

# Cluster all significant motifs based on scale(-log10(evalue))
motif_enrichments <- list(`increased RUNX1` = ame, `decreased RUNX1` = amf)

unique_motifs <- unlist(lapply(motif_enrichments, function(x) {
  x %>%
    filter(adj.pvalue < 10^-10) %>%
    arrange(evalue) %>%
    head(., 20) %>%
    pull(motif_id)
})) %>% unique()

data__enrichment_motifs <-
  lapply(names(motif_enrichments), function(x) {
    motif_enrichments[[x]] %>%
      dplyr::filter(adj.pvalue < 0.05) %>%
      mutate(cluster = x)
  }) %>%
  bind_rows() %>%
  mutate(motif_id = gsub("_.*", "", motif_id), motif_id = gsub("BC11A", "BCL11A", motif_id)) %>%
  group_by(cluster) %>%
  slice_head(n = 15) %>%
  pivot_wider(names_from = cluster, id_cols = "motif_id", values_from = "tp_percent", values_fill = 0) %>%
  column_to_rownames("motif_id")

hm_text <- 5

Figure_7C <-
  Heatmap(
    as.matrix(data__enrichment_motifs),
    clustering_method_rows = "ward.D2",
    cluster_rows = TRUE, cluster_columns = FALSE,
    col = c("black", "yellow"), show_row_names = TRUE,
    heatmap_legend_param = list(
      legend_direction = "horizontal",
      legend_width = unit(4, "cm"),
      legend_position = "bottom",
      labels_gp = gpar(fontsize = gg2gp(hm_text)),
      color_bar = "continuous",
      title_position = "topcenter",
      title_gp = gpar(fontsize = gg2gp(hm_text))
    ),
    name = "percent regions with motif"
  )

pdf(NULL)
Figure_7C <- draw(Figure_7C, heatmap_legend_side = "top")
dev.off()

#> png 
#>   2

Figure_7C